Understanding the Interaction between Interests, Conversations and Friendships in Facebook
This work addresses the problem of understanding complex social interactions on Facebook for researchers and platform analysts, though it appears incremental as it builds on existing latent space modeling approaches.
The paper tackled the challenge of integrating multiple data modalities (text, network links, categorical labels) from Facebook to study user interests, conversations, and friendships, using a novel latent space model that revealed surprising insights about social trends on the platform.
In this paper, we explore salient questions about user interests, conversations and friendships in the Facebook social network, using a novel latent space model that integrates several data types. A key challenge of studying Facebook's data is the wide range of data modalities such as text, network links, and categorical labels. Our latent space model seamlessly combines all three data modalities over millions of users, allowing us to study the interplay between user friendships, interests, and higher-order network-wide social trends on Facebook. The recovered insights not only answer our initial questions, but also reveal surprising facts about user interests in the context of Facebook's ecosystem. We also confirm that our results are significant with respect to evidential information from the study subjects.